XBRL is an Extra Fancy Knowledge Graph

Someone pointed out that a knowledge graph is just a fancy type of database.  Unlike a relational database that structures data into tables, a graph database structures data into a graph.  Doing this enables new types of pattern-based capabilities with no programming overhead necessary.

A knowledge graph is a web of meaning that describes the knowledge for some area of knowledge. A graph, per graph theory, is an approach to representing something and is made up of nodes which represent things and edges which represent associations between things. Things represented as nodes can be categorized.  The types of associations between things can also be categorized.

Two things with an association between them (e.g. two nodes with an edge between them) simply means "these two things are connected".  A knowledge graph explains how and why they are connected. Explicit labels on each node and edge tell us exactly what each node and edge in a knowledge graph means. This meaning is what turns a graph into a knowledge graph, the capturing of associations, categories, rules, and other meaning.

This meaning is not represented haphazardly. An ontology or other practical ontology-like thing which is a rulebook that explains how the nodes and edges that represent the things and associations needs to be constructed. This ontology-like thing based distillation of rules that makes up the rulebook which prevents wild behavior is effectively a schema which explains the knowledge graph. The ontology-like thing provides a description, an explanation, and serves as a specification for the semantic scaffolding which makes sure the knowledge in the knowledge graph adheres to  agreed upon meaningfully structured associations.  A complete, consistent, and precise knowledge graph represented in a form that a machine such as a computer can understand enables those machines to make logical inferences about the knowledge represented in the knowledge graph using a reasoner.

XBRL fits that above description.  Excel is not a knowledge graph.  Also, not all knowledge graphs are the same.

Think about something.  Why would ISO which has the SQL standard for relational databases spend the time to create a GQL standard for graph databases?  Why would they take the time to do that? (For the answer, see page 11, the section Relational Databases Lack Relationships)  Graph databases have different capabilities than relational databases.

XBRL is a collaborative institutional effort to create a global standard approach to representing a high quality knowledge representation. XBRL was started by the American Institute of Certified Public Accountants which is now called the Association of International Certified Professional Accountants (AICPA), the IFRS Foundation, the Big 4 professional services firms, with some software companies also involved. 

XBRL is a professional quality knowledge graph. XBRL is an extra fancy knowledge graph.  

Now, you could also represent a knowledge graph using RDF, GQL, SQL, Prolog, Datalog, JSON, JSON-LD, or probably there are even more possibilities.  All of these possible knowledge representation approaches can be grouped into three buckets:

  • Semantic web stack (W3C standard)
  • Graph databases (ISO standard GQL)
  • Logic programming (ISO standard PROLOG, ISO standard SQL, Modern Prolog, Includes global standard XBRL)
So what is so extra fancy about global standard XBRL as a knowledge graph?  Well, here is a list of what XBRL provides as I see it.
  • Dimensional fact model: XBRL provides a dimensional fact model via XBRL Dimensions. You could create a dimensional fact model using other approaches to creating knowledge graphs; but XBRL provides this out of the box.
  • Defines fact: XBRL explicitly defines the notion of a fact.  So in terms of the atomic design methodology, XBRL is defined at the "molecular" level rather than the "atomic" level like RDF. This means two things.  First, XBRL is a little bit easier to work with than RDF because there is a higher level of objects to work with.  On the other hand, XBRL is not as flexible as RDF.  XBRL is specifically "tuned" for business reports and financial reports.
  • Mechanism for generating facts: XBRL has a mechanism for the generation of new facts from existing facts or information you provide.  See XBRL Formula, specifically Formula.
  • Lots of software: There is a lot of software that works with XBRL, conformance suites for testing software to make sure it works correctly, and there is a lot of open source software such as the open source XBRL processor Arelle. BREL is another open source XBRL processor.
  • Lots of use globally: As of this writing there were 216 different XBRL projects in about 60 different countries that make use of the XBRL global standard.
  • High-level logical model: XBRL provides, but does not necessarily define very clearly, a high level model of a business report.  That same high-level logical model is used by the US Securities and Exchange Commission (SEC), European Single Market Authority (ESMA), European Financial Reporting Advisory Group (EFRAG), etc.
  • Microformat: XBRL provides the capability, similar to microformats, to embed semantic information within an HTML document.  XBRL calls this Inline XBRL. 
  • Guidance: There is a boatload of guidance provided to help you use XBRL correctly such as the Seattle Method and the forthcoming OMG Standard Business Report Model (SBRM). While the Seattle Method is more for financial statements, the ideas can also be applied to general business reporting.
  • Leverageable semantics: Foundation level semantics is defined such as units that can be used to describe facts, useful data types, functions, roles and arcroles, and other such semantics. For example, these are accounting related semantics.
  • Global context: Information in XBRL can be represented with global context using identifiers and namespaces like an IRI.
  • Approachable to business professionals: Because you can work at a higher level of abstraction, even if you like to tinker with the XBRL technical syntax level, that is possible by a motivated business professional. Business professionals can work with blocks of information, like Legos.
  • Professional quality: XBRL provides professional quality knowledge graphs. Reliable artificial intelligence requires reliable machine-interpretable knowledge.
Like many other creations; an XBRL-based knowledge graph is a combination of existing technologies and ideas assembled in a novel way that oftentimes looks obvious in hindsight.  XBRL is not limited to  financial information; both financial and nonfinancial information can be in an XBRL-based knowledge graph. See this Showcase of XBRL-based Digital Report Capabilities.

And so, there you have it.  XBRL is an extra fancy knowledge graph. Sure, you could very likely build everything that XBRL provides from scratch.  But why?


Digital proficiency is the measure of capacity to use digital technologies to one’s benefit. Step 1 is literacy.  Step 2 is fluency.  Step 3 is mastery.  How is your digital proficiency?

Additional Information:

Comments

Post a Comment

Popular posts from this blog

Microsoft CEO: "AI Agents will Replace All Software"

Image
We have entered the "agentic era"; the era of the agent or intelligent software agent . Others refer to this as the "algorithmic economy".  Maybe think many very intelligent  microservices  exchanging information with each other and can work globally. In an interview with BG2, Microsoft CEO Satya Nadella predicts that (paraphrasing) SaaS (software as a service) is dead and that AI agents will replace all software. The entire interview is the first link in "Additional Information" below.  These three videos provide a focused look at this statement: LinkedIn post including short video AI Insights Explorer video  (Microsoft CEO Predicts: "AI Agents Will Replace ALL Software") Microsoft CEO's Shocking Prediction: "Agents will replace ALL software" Here is my personal interpretation of what Satya Nadella is saying and what it means.  First, Nadella says that we are "on the brink of a monumental shift".  I agree with that statem...
Read more

New Tool for Accountants, Auditors, Analysts

Image
A new type of tool will be available (is becoming available) to support professional accountants.  This tool is somewhat similar to the traditional electronic spreadsheet but it works differently and provides different capabilities to professional accountants. This new tool will supplement the arsenal of tools available to accountants, auditors, and analysts. This new tool is semantic oriented, model-based, and a global standard technical format.  To describe this tool in the past I have used the terms " modern spreadsheet " and " professional knowledge graph " and " deductive apparatus " and " semantic oriented accounting and audit working papers " and " knowledge product " and " logical spreadsheet " and " logic programming " and " logical digital twin of financial reports ". This new type of tool will help usher in modern accountancy . In this new era, machines will support accountants, auditors, and ana...
Read more

Named Graphs

Image
A named graph is a concept in the Semantic Web architecture where a set of Resource Description Framework ( RDF ) statements (a graph) are identified using an internationalized resource identifier ( IRI ). This allows for descriptions to be made about that set of statements, such as context, provenance information, or other metadata. In an RDF database, a named graph is essentially a subset of data that has been given a unique label (referred to as a name). A graph database can contain multiple named graphs alongside its default graph, and each fact can be present in or absent from any graph. Named graphs form a patrician of information that can be used. Named graphs are useful for managing sets of RDF data within an RDF store, enabling fine-grained access control, and managing chains of provenance for pieces of data. Additional Information : Named Graphs (ScienceDirect) Graph Search Algorithms: Developer's Guide Named Graphs, Chapter 5. Data Management Patterns Wolfram, Nonahed...
Read more